Integrated transcriptome and hormone profiling highlight the role of multiple phytohormone pathways in wheat resistance against fusarium head blight
Bibliographic record
Abstract
Fusarium head blight (FHB or scab) caused by Fusarium spp. is a destructive disease of wheat. Since the most effective sources of FHB resistance are typically associated with unfavorable agronomic traits, breeding commercial cultivars that combine desired agronomic traits and a high level of FHB resistance remains a considerable challenge. A better understanding of the molecular mechanisms governing FHB resistance will help to design more efficient and precise breeding strategies. Here, multiple molecular tools and assays were deployed to compare the resistant variety Sumai3 with three regionally adapted Canadian cultivars. Macroscopic and microscopic disease evaluation established the relative level of Type II FHB resistance of the four varieties and revealed that the F. graminearum infection process displayed substantial temporal differences among organs. The rachis was found to play a critical role in preventing F. graminearum spread within spikes. Large-scale, organ-specific RNA-seq at different times after F. graminearum infection demonstrated that diverse defense mechanisms were expressed faster and more intensely in the spikelet of resistant varieties. The roles of plant hormones during the interaction of wheat with F. graminearum was inferred based on the transcriptomic data obtained and the quantification of the major plant hormones. Salicylic acid and jasmonic acid were found to play predominantly positive roles in FHB resistance, whereas auxin and ABA were associated with susceptibility, and ethylene appeared to play a dual role during the interaction with F graminearum.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".